Real-time diffraction computed tomography data reduction.

Area of Science:

• Materials Science
• Crystallography
• X-ray Imaging

Background:

• Diffraction imaging utilizes crystallographic data (cell parameters, orientation) for pixel-by-pixel image creation.
• Current synchrotron X-ray sources and detectors enable rapid data acquisition, but real-time data reduction is a bottleneck.
• Existing data analysis pipelines struggle to keep pace with the high data rates.

Purpose of the Study:

• To benchmark the performance of a standard data analysis pipeline for diffraction imaging.
• To propose novel, computationally efficient methods for real-time data reduction.
• To leverage graphics processing units (GPUs) for accelerated decoding of crystallographic information file (CIF) binary images.

Main Methods:

• Benchmarking of a typical diffraction imaging data analysis pipeline.
• Development and testing of GPU-accelerated algorithms for decoding CIF binary data.
• Real-time data reduction strategies using high-performance computing.

Main Results:

• The study quantifies the performance limitations of current diffraction imaging data analysis pipelines.
• Proposed GPU-based techniques demonstrate potential for significant acceleration of data decoding.
• Feasibility of real-time data reduction for diffraction imaging experiments is shown.

Conclusions:

• Real-time data reduction is achievable for diffraction imaging experiments with optimized computational strategies.
• GPU acceleration offers a disruptive solution to current data transfer and processing bottlenecks.
• These advancements will enhance the efficiency and scope of synchrotron-based X-ray imaging research.